15 Data Science Applications In 2024

Elon Musk purchased Twitter for $44 billion. That’s a massive amount, right? So, why was a company with no tangible products valued so high? It is because of its data. 

Data is the most powerful commodity in today’s technology-driven economy. It is the foundation of all the megatrends. Everything from social media campaigns to cloud computing to gaming uses data. All of this data is analyzed using scientific methods, also referred to as data science. In this blog, we will learn about data science applications and how they impact the world.

What is Data Science?

Data science allows a user to process large amounts of data from any repository by applying various scientific methods. There is a wide range of data science applications as almost every industry works using data. Businesses all over the world are exploring various applications of data science to help their industry grow faster. 

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Applications of Data Science

Although the applications of data science are seen in almost every industry, the following are the key players.

1. In Healthcare

The pharmaceutical and healthcare industry takes insights from applications of Data Science to study past trends and make informed decisions based on each patient’s history. This allows these industries to provide the best care to their patients. Here’s how they use data science:

• Medical Image Analysis – This is done by machine learning methods, content-based medical image indexing, and wavelet analysis for disease detection.
• Genetics & Genomics – Data science techniques are used to integrate an individual’s personal genetic information. This genomic data helps establish the impact of DNA on a patient’s health. It further helps provide a deeper understanding of genetic issues and the types of drugs that would better impact a patient. This allows doctors to provide personalized patient care.
• Drug Development – The development of any drug is a long and expensive process. However, the applications of data science have comparatively made the process simpler and shorter. Data science uses advanced mathematical modeling to predict the efficacy of a drug. Computer model simulations are replacing lab experiments as they are cost-effective and time-efficient.

Example: Doctors can scan pictures of cancer-inducing cells and analyze these with data science technology to identify if the patient has cancer or not. This helps in taking preventive measures and providing timely healthcare support. 

2. Image Recognition

Have you ever noticed how Google Photos recognizes people from your family and friends from the pictures stored in your gallery? It recognizes a face/image and groups all the pictures of that one specific person in a single folder. This is done by machine learning. 

Data science helps this process by collecting data or information about that person through synced Gmail accounts. Facebook works with a similar model when it asks you to tag people in the photos you share.

Example: You have posted a picture on your social media account. The image will be recognized with the help of machine learning and data science and analyzed to check if the faces in the picture match your social media friends. If there is a match, you will receive an auto-tagging suggestion. 

3. Speech Recognition

Voice assistants like Siri, Alexa, Google, etc. have become a part of our day-to-day lives. These assistants can make phone calls, play music, and even schedule appointments for you based on your schedule. This is made possible through data collected, stored, and used by various apps on our phones to serve us personalized content. 

Example: You are on a trip to Nepal and want to know the popular restaurants you can dine in. When you ask for restaurant information from Siri, it uses advanced speech recognition technology powered by data science to accurately decipher your spoken query and understand your preferences. Eventually, it will suggest restaurants you can visit in the location.

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4. Fraud & Risk Detection

Financial companies struggle with a lot of paperwork. They have to manage the data of various customers and clients, maintain records, streamline the loan sanctioning process, and more. Since the introduction of data science in the industry, this process has become simplified. Data science helps these companies to –

• Separate various data sets using filters.
• Help with customer profiling.
• Assist in analyzing past expenditures.
• Help analyze risk probabilities to avoid long-term losses.

Example: A stock brokerage company utilizes data science techniques to predict market trends, personalize client recommendations, and optimize their trading strategies. Large historical datasets and machine learning algorithms are used to enhance decision-making and maximize profitability in the financial markets.

5. Airline Route Planning

Airlines typically struggle with high operational costs and steep air-fuel prices. This can lead to low operating profits in the long run if not managed properly. With data science applications, airlines can effectively leverage customer databases and their balance sheets to offer efficient deals to customers. 

Example: An airline wants to plan a route for their new flight. It can utilize data science to identify the shortest route possible, if it has to be a direct route or there is a need for connecting flights, or if the flight can halt between destinations.

6. Government

Governments all over the world collect relevant data about their citizens for the purpose of administration. In India, the Aadhaar card is a single document recognized for the identification and authentication of citizens. It provides information about the age, demographic, sex ratios, etc., of the citizens. This comprehensive database is maintained for the government to be able to make effective policies and take important decisions. 

Aadhaar card is also linked with PAN cards and various online payment channels. This allows the revenue department of the government to build multidimensional taxpayer profiles to prevent tax evasion. These agencies are also able to forecast individual tax returns through machine learning using individuals’ tax-paying histories.

Example: The government wants to introduce a new housing scheme for women in the age bracket of 25 to 40. With the help of data science techniques and tools, the government can analyze the population distribution in the provided age bracket to determine the beneficiaries. Further, with predictive modeling, the government can predict future housing demands among women aged 25 to 40 based on factors, such as economic trends, population growth, and migration patterns.

Also Read: Data Science Tools

7. Social Media

Have you ever noticed how Instagram and Facebook recommend people, pages, and various businesses to follow? These are usually people you know or businesses whose products you like or have purchased in the past. This is done using data science applications. The data is also sold to various e-commerce platforms to send you customized advertisements and recommendations.

Example: Let’s assume you recently opened an Instagram account and most of the reels you interacted with were related to cooking. Now, Instagram analyzes your preferences with the help of data science techniques and personalizes your feed to show you trending recipes, popular cooking techniques, and other similar content. Additionally, Instagram will also tailor your feed according to your recent preferences. If you start watching DIY craft videos, the algorithm will show you more of these videos.

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8. Targeted Advertising/Recommendation

Targeted advertising is a type of online advertising that leverages data science to ensure relevant advertisements are shown to the audience. It delivers promotional content to customers according to their behavior or preferences. 

Example: You went on a website to shop for clothes and browsed through a shirt catalog, but you did not order it. After a week, it is the festive season and suddenly you see the very same shirt you had clicked on the website. This is because the website used data science analytical techniques to analyze your shopping behavior and concluded that you were interested in the shirt. 

9. Logistics and Transport

Companies like DHL, FedEx, etc. optimize pricing, structure delivery routes, and determine resource allocation plans using data science. They also take the help of big data to achieve the following:

• Determine the best time suited for delivery.
• Find efficient routes for the shipment of products.
• Find the best mode of transport possible for timely and efficient delivery.

Data science applications have created a stronghold in the transportation industry through extensive analysis of fuel consumption patterns, driver behavior, and vehicle monitoring. EVs are the biggest advancement in the field of transportation. Vehicle manufacturers like Tesla are now creating intelligent automobiles and designing better logistical routes using data science. 

Popular cab services like Ola, Uber, etc. also use data science to provide optimal customer experience to users by analyzing customer profiles and locations, suggesting rides, and determining appropriate fares. 

Example: Supposedly, a food delivery app receives an order. Data science will optimize the delivery process by analyzing real-time data on order volume, traffic conditions, and delivery routes. With a predictive algorithm that can anticipate demand, the delivery personnel will be dispatched in time. Further, machine learning models will improve delivery time estimates based on historical data. 

10. Augmented Reality

Augmented reality is an interactive experience where digital content is overlaid onto real-life environments or objects. The best example of data science and virtual or augmented reality coming together is in a VR set. VR headsets contain computing knowledge, algorithms, and various personalized data to enhance a user’s viewing experience.

Example: Interactive games like Pokemon GO use data from the local authorities to give their users the ability to walk around and look at Pokemon on walls, streets, and things that don’t exist.

11. Gaming

Games are designed using machine learning algorithms that improve or upgrade themselves as the player moves up to higher levels. In motion gaming, the computer analyzes your previous moves and enhances the game accordingly. Top gaming companies are leveraging various data science applications to offer a high-level immersive experience to their users. 

Example: An online tennis game where a single player can compete against the computer is an example of how data science provides an immersive experience to players. With performance analysis, the algorithm will understand the movement patterns of the player, their strategy preferences, shot accuracy, reaction time, and other metrics. Next, with predictive analytics, the system will anticipate the player’s move and plan its moves accordingly. Further, it will enhance gameplay in the next levels according to the player’s skills. 

12. In Banking

Data science has enabled banks across the globe to be more secure and manage their resources efficiently to serve customers better. Data science has allowed banks to perform the following functions effectively:

• Analyze past data to make strategic decisions and avoid losses.
• Efficiently manage customer data.
• Carry out risk analyses to identify defaulters. 
• Make informed forecasts regarding company growth.
• Design personalized wealth plans for customers.

Example: A bank, with the help of data science and machine learning techniques, can analyze large amounts of transaction data in real time. This analysis helps in identifying patterns of customer spending behavior and flag potential fraudulent transactions for further investigations. Data science, thus, helps in preventing financial losses in the banks. 

13. E-Commerce

E-commerce websites buy data from search engines and social media platforms to provide personalized advertisements and recommendations to relevant users. This is the biggest advantage that companies today use. They market their products to people who actually want them instead of everyone. E-commerce platforms also use various applications of data science to examine the following: 

• Examine customer purchase patterns.
• Analyze online surveys.
• Understand customer feedback.
• Analyze new trends.
• Identify popular products.
• Optimize pricing structures. 

Example: You did a Google search for the type of journals available in the market. When you visit your usual e-commerce platform, you will see journal suggestions. This is because the e-commerce website, with the help of data science techniques, has analyzed what you are looking for. You browse through some journals and add one to your wish list. Soon after, you will start receiving notifications about price drops for the product. 

14. Education

Education, as a field, has been under constant renovation ever since the advent of online education. Universities and schools are using data science to measure faculty performance and monitor student requirements. The curriculum is also being constantly updated using recent trends and updates. Online platforms that house a variety of online courses use this discipline more actively, by using insights and demographics to curate better courses with updated knowledge.

Example: A college allows students with 75% attendance to sit in the annual examinations. With predictive analytics, the college can identify the students who are most likely to be ineligible for examinations. This can allow the institution to take preventive measures beforehand and provide targeted assistance to students and enhance the retention rate. 

15. Manufacturing

Many high-performing manufacturing firms are trying to optimize their manufacturing processes with the help of automation and robotics. Technologies like the Internet of Things (IoT) are also enabling industries to predict potential problems, monitor systems, and analyze the continuous stream of data. 

Data science helps manufacturing and supply chains to optimize energy costs and improve the decision-making process. It is also used to build an autonomous system using historical and real-time data to boost the manufacturing line.

Example: A car manufacturing unit has recently installed several new equipment replacing the old ones that broke down due to not being repaired in a time-bound manner. Therefore, the owner utilized data science techniques like predictive maintenance to analyze data from sensors in the machinery and get timely indications of potential problems to initiate repair early. This reduces downtime and maintenance costs, leading to increased productivity. 

Conclusion

Almost every industry in the world requires data science to streamline its process, avoid losses, and make forecasts. This has made data science one of the most sought-after skills in the world today. Since the applications of data science in the world are so diverse, let us know in the comment section below about some of the other data science applications that we may have missed.Are you looking forward to pursuing a career in data science? If yes, learn more about data science career prospects. Additionally, check out top data science projects based on your experience level to enhance your portfolio.

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